Deepfake Detection using Deep Learning

Deepfake technology, driven by generative adversarial networks (GANs), poses significant challenges in digital security, misinformation, and privacy. Detecting deepfakes in images and videos requires advanced deep learning models. This study explores deepfake detection using convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer-based architectures like Vision Transformers (ViTs). We employ Meso4_DF deepfake detection pipeline that uses TensorFlow/Keras, PyTorch, OpenCV for processing, with Dlib, Scikit-Image, and NumPy for feature extraction, leveraging transfer learning for enhanced accuracy. Video analysis includes frame extraction and temporal feature learning using LSTMs and 3D CNNs. Experimental results demonstrate that deep learning-based methods achieve high accuracy in distinguishing real and manipulated media, offering a robust approach to deepfake detection.